The Digital Signal Processing Group is carrying out research in the general area of digital signal processing. While a major part of our activities focus on the development of new algorithms, there is a strong conviction that theoretical developments must be closely tied to applications and to issues of implementation. The application areas which we deal with principally are speech, image and geophysical data processing. In addition to specific projects being carried out on campus, there is close interaction with Lincolr, Laboratory and with the Woods Hole Oceanographic Institution. In the area of speech processing, we have over the past several years worked on the development of systems for bandwidth compression of speech, parametric modeling of speech using pole-zero models, timescale modification of speech and enhancement of degraded speech. Recently we have obtained some important new results on timescale modification of speech, growing out of a more general set of issues involving the estimation of a signal after its short-time Fourier transform has been modified. We are also exploring new techniques for speech enhancement using adaptive noise cancelling when multiple microphones are available. There are also a number of projects related to image processing that we are currently pursuing. One project is restoration of images degraded by additive noise, multiplicative noise, and convolutional noise. Out of this project, we have developed a new image restoration system which is applicable to restoring images degraded by various different types of degradation. Our current work in this project involves development of new image restoration systems by exploiting additional available information such as the range map in infrared radar images. Another project is development of new image coding techniques by reducing quantization noise in PCM image coding or by reducing blocking effect in cosine transform image coding. Our approach to first decorrelate the quantization noise, and then reduce the quantization noise by a noise reduction system, led to a noticeable improvement in the performance of a simple PCM image coding system. We are currently working on the extension of these results to a more complex PCM image coding system. To reduce the blocking
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